GRASP: group-Shapley feature selection for patients
1 Chinese Academy of Medical Sciences \& Peking Union Medical College, Beijing, China 2 Queen's University, Belfast, United Kingdom. 3 Heidelberg University, Heidelberg, Germany. Email
Feature selection remains a major challenge in medical prediction, where existing approaches such as LASSO often lack robustness and interpretability. We introduce GRASP, a novel framework that couples Shapley value driven attribution with group $L_{21}$ regularization to extract compact and non-redundant feature sets. GRASP first distills group level importance scores from a pretrained tree model via SHAP, then enforces structured sparsity through group $L_{21}$ regularized logistic regression, yielding stable and interpretable selections. Extensive comparisons with LASSO, SHAP, and deep learning based methods show that GRASP consistently delivers comparable or superior predictive accuracy, while identifying fewer, less redundant, and more stable features.
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Department of Industrial and Systems Engineering, University of Southern California（1 工业与系统工程系，南加州大学）；Colorectal Research Center, Iran University of Medical Sciences（2 肠道癌研究中心，伊朗医学科学大学）；Department of Health Science, California State University, Long Beach (CSULB)（3 健康科学系，长滩加州州立大学（CSULB））；Department of Industrial and Manufacturing Engineering, California State Polytechnic University, Pomona（4 工业与制造工程系，庞纳加州州立理工大学）
Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans
Department of Biomedical Engineering, Virginia Commonwealth University（弗吉尼亚联邦大学生物医学工程系）；Department of Biomedical Engineering, Case Western Reserve University（凯斯西储大学生物医学工程系）；Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center（克利夫兰医学中心哈灵顿心脏和血管研究所）；BHF Centre for Cardiovascular Science, University of Edinburgh（爱丁堡大学BHF心血管科学中心）；Department of Radiology, Case Western Reserve University（凯斯西储大学放射学系）
How important are the genes to explain the outcome - the asymmetric Shapley value as an honest importance metric for high-dimensional features
Dept of Epidemiology and Data Science, Amsterdam Public Health research institute, Amsterdam University Medical Centers（流行病学与数据科学系，阿姆斯特丹公共卫生研究所，阿姆斯特丹大学医学中心）；Norwegian Computing Center（挪威计算中心）
Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions
University of Pittsburgh（匹兹堡大学）；Beijing Information Science & Technology University（北京信息科技大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）；Astrata Solutions（阿斯特拉塔解决方案公司）
Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Breast Cancer; Dictionary Version BM1.0
Hamadan University of Medical Sciences（哈马丹医科大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Motamed Cancer Institute（莫塔梅德癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；Technological Virtual Collaboration (TECVICO Corp.)（技术虚拟协作公司）
Machine Learning-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database
University of Southern California（南加州大学）；Iran University of Medical Sciences（伊朗医学科学大学）；California State University（加利福尼亚州立大学）；California State Polytechnic University（加利福尼亚州_polytechnic大学）
Enhancing Interpretability of AR-SSVEP-Based Motor Intention Recognition via CNN-BiLSTM and SHAP Analysis on EEG Data
School of Control Science（控制科学学院）；Engineering Shandong University Jinan, Shandong（山东大学工程学院济南山东）；Shandong Inspur Science Research Institute Co., Ltd. Jinan, Shandong（山东 Inspur 科研院有限公司济南山东）
A Data-Driven Approach to Support Clinical Renal Replacement Therapy
Department of Applied Science and Technology（应用科学与技术系）；Department of Inf. Eng., Computer Science and Mathematics（信息工程、计算机科学与数学系）；University of L'Aquila（拉奎拉大学）；Dipartimento di Scienze della Salute（健康科学系）；University of Florence（佛罗伦萨大学）
